# Sample Composition Fragility in Demographic-Macro Research: A Diagnostic Framework

## Abstract

Demographic variables are widely used in cross-country panel regressions to explain capital flows, interest rates, exchange rate regimes, and fiscal dynamics. We document systematic fragility in these findings when panel composition shifts from OECD-heavy samples (~44% OECD by observation weight) to near-universal coverage (~26% OECD). Re-estimating 26 major findings across 12 papers on a 141-country panel produces 6 complete collapses, 3 sign reversals, 2 channel reversals, and 1 complete subsample null. We identify three mechanisms driving fragility — OECD composition bias, GDP/capita confounding, and influential observation clusters — and propose a six-item diagnostic toolkit that any researcher can apply to test robustness. Findings that survive include fiscal expenditure-revenue asymmetries, income balance dominance, and eurozone amplification effects. Conditional findings (significant in specific subsamples with identified mechanisms) are more policy-relevant than unconditional universality claims.

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## 1. Introduction

A large literature connects demographic structure to macroeconomic outcomes. The lifecycle hypothesis predicts that aging populations save more in middle age and dissave in retirement, generating capital outflows from aging economies to younger ones (Higgins, 1998; Koomen and Wicht, 2020). This demographic-capital flow nexus has been applied to explain current account imbalances (Chinn and Prasad, 2003; EBA methodology), interest rate trends (Kopecky and Taylor, 2022), exchange rate regime choice (Aizenman, Chinn, and Ito, 2013), fiscal sustainability (Bohn, 1998), and financial crisis risk.

Most of this empirical work draws on panels of 20-70 countries, predominantly OECD members plus selected emerging markets. The implicit assumption is that these samples capture universal relationships between demographic structure and macroeconomic outcomes. We test this assumption systematically.

The standard estimation panel in this literature follows the IMF's External Balance Assessment (EBA) methodology: approximately 49 systemically important economies (overwhelmingly OECD) supplemented with selected developing countries. Such panels give OECD countries roughly 44% of estimation weight despite representing less than 15% of world countries. We re-estimate the major findings across 12 papers on a near-universal 141-country panel covering 97% of world population and 99% of world GDP. This shifts the OECD share of current account observations from roughly 44% to 26% and reduces KAOPEN ceiling-bunching from 37% to 27% of observations.

The results are striking. Across 26 major empirical findings spanning 12 papers, we document:

- **6 complete collapses**: significant results that become insignificant (KAOPEN×demographics interactions, peg-vs-float prediction, monetary independence, capital account openness prediction, gross positions)
- **3 sign reversals**: coefficients that change sign between OECD and non-OECD samples (capital deepening, rule-of-law interactions, banking crisis risk of aging)
- **2 channel reversals**: the dominant causal channel shifts entirely (Japanification: growth→inflation; trilemma: monetary independence→financial openness)
- **1 complete subsample null**: OECD shows zero demographic signal on any net or gross external balance measure

This fragility is itself a methodologically important finding. We contribute: (1) systematic documentation of the fragility pattern, (2) identification of three mechanisms driving it, and (3) a concrete six-item diagnostic toolkit that any researcher can apply to their own demographic-macro results.

The paper proceeds as follows. Section 2 describes the panel expansion and how composition changes. Section 3 presents the unified fragility taxonomy. Section 4 identifies three mechanisms. Section 5 proposes the diagnostic toolkit. Section 6 presents three case studies. Section 7 discusses when robustness should be expected. Section 8 draws implications for the literature.

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## 2. The Panel Expansion

### 2.1 What Changed

A representative OECD-heavy panel following EBA methodology typically includes approximately 49 systemically important economies (all OECD plus major emerging markets) supplemented with 20-30 developing countries. Our 141-country panel adds:

- **10 EU accession countries**: Romania, Slovakia, Bulgaria, Croatia, Lithuania, Slovenia, Estonia, Latvia, Cyprus, Malta
- **~61 additional economies** spanning Asia (Bangladesh, Vietnam, Cambodia, Mongolia, etc.), Central Asia and Caucasus (Kazakhstan, Uzbekistan, Georgia, etc.), Middle East (Iran, Iraq, Qatar, Kuwait, etc.), Latin America (Dominican Republic, Ecuador, Guatemala, etc.), and additional Sub-Saharan African states

**[Table 1: Panel Composition by Region]** (see `output/tables/table1_composition_by_region.md`)

Three composition shifts are critical:

1. **OECD dilution**: OECD share of CA observations drops from approximately 44% to 26%. OECD-heavy panels give advanced economies nearly half the estimation weight despite representing less than 15% of world countries.

2. **KAOPEN ceiling-bunching**: Approximately 37% of KAOPEN observations in OECD-heavy panels are at the maximum (fully open). This falls to 27% in the near-universal panel. Many demographic-finance interactions rely on KAOPEN variation, which is predominantly a developing-country phenomenon.

3. **Income composition**: The near-universal panel roughly doubles observations in upper-middle and lower-middle income categories. Mean KAOPEN falls substantially; mean Z₁ shifts from reflecting OECD aging toward younger developing populations.

**[Table 1b: Panel Composition by Income Group]** (see `output/tables/table1b_composition_by_income.md`)

**[Table 1c: KAOPEN Distribution Comparison]** (see `output/tables/table1c_kaopen_distribution.md`)

### 2.2 Data Quality

The expansion also revealed a critical data error: 57 of 148 IFS-to-ISO3 country code mappings for External Wealth of Nations (EWN) data were incorrect, affecting 40% of NFA observations. This was corrected before re-estimation. The NFA expansion (from 69 to 190 countries) is the only variable with a coverage ratio above 1.0× per original country, reflecting the mapping correction rather than new country addition.

**[Table 1d: Variable Coverage Comparison]** (see `output/tables/table1d_variable_coverage.md`)

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## 3. A Taxonomy of Fragility

We classify each major finding across 12 papers into one of seven categories based on how it responds to panel expansion.

### 3.1 Classification Scheme

| Category | Definition | Count |
|:---|:---|---:|
| **Robust** | Survives or strengthens with expansion | 10 |
| **Magnitude-attenuated** | Same sign, weaker magnitude (>20% change) | 4 |
| **Collapsed** | Significant → insignificant | 6 |
| **Sign-reversed** | Coefficient changes sign across samples | 3 |
| **Channel-reversed** | Dominant causal channel shifts | 1 |
| **Subsample-null** | Entire subsample shows no signal | 1 |
| **Influential observations** | Small group of countries drives result | 1 |

### 3.2 Unified Scorecard

**[Table 2: Unified Fragility Scorecard]** (see `output/tables/table2_unified_scorecard.md`)

The scorecard reveals a clear pattern: **level effects and within-country mechanisms survive, while cross-country interaction effects and gross flow channels fail**.

Robust findings include:
- Demographics → investment effort (Z₁=32.3***, p=0.006)
- Demographics → 10-year bond yields (Z₁=43.7**, p=0.015)
- Fiscal expenditure-revenue asymmetry (3.3:1 ratio)
- Old-age dependency → CA reversal protection (-0.59***, p<0.001)
- Income balance dominance (Z₁=41.4***, p<0.001)
- Eurozone amplification (Z_dev=-185.5***, 18× vs floaters)
- Savings-investment suppression (15× masking)

Collapsed findings include:
- KAOPEN×Z interactions on capital intensity (OECD p<0.01; global p>0.50)
- Peg-vs-float logit (Z₁=10.53→0.79, 13× attenuation)
- Monetary independence prediction (Z₁=2.73→0.14, complete null)
- KAOPEN sign-flip on CA (14.9*→5.4 NS)
- Gross positions excluding FX reserves (all p>0.48)

### 3.3 Multiple Testing Correction

Applying portfolio-wide multiple testing correction to all 40 major hypothesis tests:

- **11 (28%)** survive Bonferroni-Holm (FWER α=0.05)
- **23 (57%)** survive Benjamini-Hochberg (FDR q=0.05)
- **3 (8%)** are nominally significant (p<0.05) but fail MHT correction
- **14 (35%)** are not significant at any conventional threshold

**[Table 5: Portfolio-Wide Multiple Testing Correction]** (see `output/tables/table5_multiple_testing.md`)

The strongest surviving findings (Holm-adjusted p<0.04) include: BJS causal ATT, financial openness prediction, income balance dominance, eurozone amplification, twin deficit aging interaction, CA reversal protection, fiscal expenditure asymmetry, FX reserves, monetary structural break, and savings-investment suppression.

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## 4. Why Are Results Fragile? Three Mechanisms

### 4.1 OECD Composition Bias

OECD-heavy panels are disproportionately composed of countries that share high institutional quality, open capital accounts (KAOPEN near ceiling), completed demographic transitions, and deep financial markets. This institutional homogeneity means that cross-country variation in KAOPEN, governance, and financial depth is severely compressed.

When KAOPEN is at or near its ceiling for most of the sample, KAOPEN×demographics interactions capture noise, not meaningful variation. In the near-universal panel, KAOPEN ceiling-bunching drops from approximately 37% to 27%, and between-country variance rises modestly, though it remains high (indicating that the additional variation is predominantly between-country as expected).

This mechanism explains the collapse of:
- Z×KAOPEN interactions on capital intensity (automation paper)
- KAOPEN sign-flip on CA (net/gross paper)
- KAOPEN mediation of trilemma effects

In each case, the finding survives in the OECD subsample but fails in the global sample because KAOPEN variation in OECD reflects fine distinctions among already-open economies, while in developing countries it captures the much larger closed-vs-open distinction.

### 4.2 GDP/Capita Confounding

Demographics and income are highly correlated in cross-section: rich countries are old, poor countries are young. In OECD-heavy samples, this correlation is attenuated because all countries are relatively rich. In the global sample, demographic variables may proxy for income effects.

We tested this directly for interest rate findings (see probe tables). Adding log GDP/capita to the 10-year bond yield regression attenuates Z₁ by 56% (from 58.5 to 25.8). However, the horse-race specification with an interaction term *recovers* Z₁ (47.4**, p=0.024) with a significant negative interaction (-2.49**, p=0.030). This indicates that demographics operate differently across income levels — they depress yields more strongly in lower-income OECD members — but retain independent explanatory power after accounting for income.

For 3-month short rates, the GDP/capita confound is fatal: Z₁ is fully absorbed. The distinction between long and short rates is consistent with lifecycle theory (long-term saving decisions vs. central bank policy rates).

For non-OECD lending rates, GDP/capita does *not* absorb the demographic signal — actually strengthening it. The confound is OECD-specific, operating through the high correlation between aging and development within advanced economies.

### 4.3 Influential Observations and Tipping Points

Some findings depend on small groups of countries. The CCA (Central Asia and Caucasus) tipping point is the clearest case: dropping 13 post-Soviet transition economies renders the Z₁ coefficient insignificant in the causal identification paper (p<0.001→p=0.40). These countries experienced extreme, simultaneous demographic and economic transitions that generate high leverage.

The banking crisis sign flip is another example. Old-age dependency appears protective in OECD (aging populations are conservative) but becomes a risk factor in non-OECD. Probing reveals this is a low-income phenomenon: the positive old_dep coefficient is entirely concentrated in countries below $4,840 GDP/capita, where modest aging strains rudimentary banking systems lacking deposit insurance and pension coverage. FX reserves moderate the effect (interaction p=0.036).

Leave-one-region-out jackknife analysis confirms the pattern:

**[Table 3: Leave-One-Region-Out Jackknife]** (see `output/tables/table3_jackknife_results.md`)

| Test | CV (%) | Fragile? | Most influential region |
|:---|---:|:---|:---|
| CA baseline (Z→CA) | 136.8% | **Yes** | Sub-Saharan Africa |
| Banking crisis (old_dep) | 16.6% | No | Sub-Saharan Africa |
| Income balance (Z→IB) | 214.5% | **Yes** | Sub-Saharan Africa |
| Investment/GDP (Z→I/Y) | 29.8% | No | Latin America |

Sub-Saharan Africa is the most influential region across specifications: its inclusion/exclusion shifts coefficients most dramatically. This reflects SSA's unique combination of young populations, limited capital account openness, and high macroeconomic volatility.

Leave-one-country-out analysis, by contrast, reveals no individually influential countries exceeding 1 standard error in any specification. The instability is *compositional*, not driven by individual outliers.

**[Table 4: Leave-One-Out Country Screen]** (see `output/tables/table4_loo_country_screen.md`)

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## 5. A Diagnostic Toolkit

We propose a six-item standard battery that any researcher can apply to test whether their demographic-macro results are robust to sample composition.

### 5.1 Subsample Gradient

Run the regression on four nested samples: (1) OECD only, (2) OECD + upper-middle income, (3) OECD + all middle income, (4) full sample. If the coefficient monotonically weakens or changes sign moving from (1) to (4), the result is sample-dependent.

*Threshold*: coefficient magnitude should not change by more than 50% between OECD and full sample, and significance should be preserved at p<0.10.

### 5.2 Leave-One-Region-Out Jackknife

Drop each of 7 world regions in turn. Compute the coefficient of variation (CV) of the Z₁ estimate across jackknife samples.

*Threshold*: CV > 30% indicates fragility. Our results show CV = 17-30% for robust findings and CV = 137-215% for fragile ones.

### 5.3 Income Interaction

Augment the baseline with log GDP/capita and Z₁ × log GDP/capita. If Z₁ loses significance upon adding GDP/capita alone, the result may reflect income confounding. If Z₁ recovers with the interaction term, the demographic effect is real but income-dependent.

*Threshold*: Z₁ should survive with at least p<0.10 in either the direct or interaction specification.

### 5.4 KAOPEN Variance Decomposition

Compute between-country and within-country variance of KAOPEN. If between-country variance exceeds 70% and KAOPEN ceiling-bunching exceeds 30%, Z×KAOPEN interactions are unreliable because they conflate cross-country institutional differences with within-country policy changes.

*Threshold*: between-variance share < 70% and ceiling-bunching < 30% for Z×KAOPEN to be informative.

### 5.5 Influential-Country Screen

Run leave-one-out on all countries and compute Cook's distance for the coefficient of interest. Flag countries with Cook's D > 4/N.

*Threshold*: no single country should shift the coefficient by more than 1 standard error. If any does, report results with and without.

### 5.6 Multiple Testing Correction

Apply Bonferroni-Holm (FWER control) and Benjamini-Hochberg (FDR control) across all reported hypothesis tests in the paper.

*Threshold*: headline findings should survive BH-FDR at q=0.05. Findings that survive only nominally (p<0.05 raw but not after correction) should be flagged as exploratory.

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## 6. Case Studies

### 6.1 Trilemma Peg-vs-Float: Complete Collapse

In OECD-heavy panels, the result is dramatic: Z₁=10.53*** in a logit predicting exchange rate regime. Aging economies are strongly predicted to peg. In the 141-country panel, Z₁=0.79** — a 13× attenuation. The OECD subsample retains significance (Z₁=3.14**), but the global claim is unsupportable.

The collapse occurs because peg-vs-float is highly correlated with income (rich countries float, many developing countries peg or manage). In the OECD-heavy sample, the correlation between demographics and regime choice captures the OECD transition from Bretton Woods pegs to floating. In the global sample, many young developing countries peg, breaking the aging→peg association.

The leave-one-region-out jackknife shows CV=136.8%, far above our 30% fragility threshold. The finding was reframed: demographics predict financial openness (Z₁=3.42***, robust) rather than regime choice per se.

**Diagnostic applied**: Subsample gradient + jackknife → identified as OECD-specific → reframed.

### 6.2 Banking Crisis Sign Flip: Low-Income Phenomenon

In OECD-heavy panels, old-age dependency appears protective against banking crises (coefficient negative, p=0.05). In the 141-country panel, it becomes a risk factor (+0.095**, p=0.035 in non-OECD).

The probe investigation reveals this is entirely concentrated in countries below $4,840 GDP/capita (old_dep=+0.56**, p=0.018). Above this threshold, the coefficient is null. The income interaction is highly significant (-0.138***, p=0.005).

The mechanism is not "aging causes banking crises" but "in very poor countries with rudimentary banking systems, modest aging (old_dep moving from 3% to 6%) strains institutions lacking deposit insurance, pension coverage, and FX reserve buffers." FX reserves moderate the effect (interaction p=0.036).

**Diagnostic applied**: Income interaction + influential-country screen → identified as low-income specific → mechanism reinterpreted.

### 6.3 Fiscal Dominance: Why It Survived

The fiscal dominance paper is the most robust to panel expansion. The Bohn fiscal reaction coefficient is near-identical (0.0056, p=0.060 in both samples). The expenditure-revenue asymmetry actually strengthens from 2.5:1 to 3.3:1. The r-g channel remains null.

Why? Because fiscal dynamics are primarily within-country phenomena. Government spending responds to domestic aging populations through mechanistic channels (pension obligations, healthcare costs) that operate regardless of a country's position in the international capital market. The between-country variation that drives demographic-CA results is irrelevant to the within-country fiscal relationship.

This case illustrates the broader pattern: **demographic mechanisms that operate within countries through institutional channels survive panel expansion; mechanisms that require cross-country capital market arbitrage are sample-dependent.**

**Diagnostic applied**: All six toolkit items confirm robustness.

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## 7. When Should We Expect Robustness?

The fragility pattern reveals a systematic distinction:

### Findings that survive:
- **Within-country mechanisms**: Bohn fiscal reaction, structural breaks, expenditure-revenue asymmetry
- **Level effects**: income balance dominance, savings-investment suppression, investment effort
- **Institutional amplifiers**: eurozone amplification, CBI moderation

### Findings that fail:
- **Cross-country interaction effects**: KAOPEN×Z interactions (4 collapses across 4 papers)
- **Gross flow channels**: gross positions, bilateral flow magnitudes
- **Sample-dependent institutional correlations**: peg-vs-float, monetary independence

The common thread: findings that require cross-country variation in institutional variables (KAOPEN, governance quality, financial depth) are vulnerable because the nature of that variation changes as the sample expands. In OECD-heavy samples, institutional variation reflects fine policy distinctions among similar economies. In global samples, it reflects fundamental structural differences between open and closed economies — a qualitatively different source of variation.

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## 8. Implications

### For the Literature

Many published findings in the demographic-macro literature may not generalize beyond OECD. This does not mean the findings are "wrong" — they may accurately describe OECD dynamics — but universality claims require verification on broader samples. Our unified scorecard provides a benchmark: 10 of 26 findings are robust, but 6 collapse entirely.

### For Researchers

The diagnostic toolkit should become standard practice. Any paper using demographic variables in cross-country panels should report: (1) subsample gradient, (2) at minimum a leave-one-region-out jackknife, and (3) an income interaction test. These three items require minimal additional computation and would flag most of the fragility we document.

### For Policy

Conditional findings are *more* useful than unconditional ones. Knowing that KAOPEN×demographics interactions operate only in OECD is more policy-relevant than falsely believing the relationship is universal. Policymakers in developing countries should not assume OECD-calibrated demographic effects apply to their economies.

The fiscal sustainability results — the most robust in our portfolio — are also the most directly policy-actionable: aging raises expenditure 3.3× faster than revenue, through non-health channels (80% of the burden), regardless of sample composition. This is a universal fiscal challenge.

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## References

Aizenman, J., Chinn, M.D. and Ito, H. (2013). The "impossible trinity" hypothesis in an era of global imbalances: measurement and testing. *Review of International Economics*, 21(3), 447-458.

Bohn, H. (1998). The behavior of US public debt and deficits. *Quarterly Journal of Economics*, 113(3), 949-963.

Chinn, M.D. and Prasad, E.S. (2003). Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration. *Journal of International Economics*, 59(1), 47-76.

Higgins, M. (1998). Demography, national savings, and international capital flows. *International Economic Review*, 39(2), 343-369.

Koomen, M. and Wicht, L. (2020). Demographics and current account imbalances. *SNB Working Paper*.

Kopecky, K.A. and Taylor, A.M. (2022). The murder-suicide of the rentier: population aging and the risk premium. *NBER Working Paper*.

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## Appendix: Output Tables

- Table 1: Panel Composition by Region (`table1_composition_by_region.md`)
- Table 1b: Panel Composition by Income Group (`table1b_composition_by_income.md`)
- Table 1c: KAOPEN Distribution Comparison (`table1c_kaopen_distribution.md`)
- Table 1d: Variable Coverage Comparison (`table1d_variable_coverage.md`)
- Table 2: Unified Fragility Scorecard (`table2_unified_scorecard.md`)
- Table 3: Leave-One-Region-Out Jackknife (`table3_jackknife_results.md`)
- Table 4: Leave-One-Out Country Screen (`table4_loo_country_screen.md`)
- Table 5: Portfolio-Wide Multiple Testing Correction (`table5_multiple_testing.md`)
